US20140379619A1 - Automated System For Generative Multimodel Multiclass Classification And Similarity Analysis Using Machine Learning - Google Patents

Automated System For Generative Multimodel Multiclass Classification And Similarity Analysis Using Machine Learning Download PDF

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US20140379619A1
US20140379619A1 US14/313,863 US201414313863A US2014379619A1 US 20140379619 A1 US20140379619 A1 US 20140379619A1 US 201414313863 A US201414313863 A US 201414313863A US 2014379619 A1 US2014379619 A1 US 2014379619A1
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data
sample
queue
workers
work items
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Ryan Permeh
Stuart McClure
Matthew Wolff
Gary Golomb
Derek A. Soeder
Seagen Levites
Michael O' Dea
Gabriel Acevedo
Glenn Chisholm
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Cylance Inc
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Cylance Inc
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Priority to US14/313,863 priority Critical patent/US20140379619A1/en
Assigned to CYLANCE, INC. reassignment CYLANCE, INC. ASSIGNMENT OF ASSIGNORS INTEREST (SEE DOCUMENT FOR DETAILS). Assignors: CHISHOLM, GLENN, WOLFF, MATTHEW, GOLOMB, GARY, ACEVEDO, GABRIEL, LEVITES, SEAGEN, MCCLURE, Stuart, O'DEA, MICHAEL, PERMEH, Ryan, SOEDER, DEREK
Publication of US20140379619A1 publication Critical patent/US20140379619A1/en
Priority to US15/789,765 priority patent/US11657317B2/en
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    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N20/00—Machine learning
    • G06N99/005—
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F9/00—Arrangements for program control, e.g. control units
    • G06F9/06—Arrangements for program control, e.g. control units using stored programs, i.e. using an internal store of processing equipment to receive or retain programs
    • G06F9/46—Multiprogramming arrangements
    • G06F9/50—Allocation of resources, e.g. of the central processing unit [CPU]
    • G06F9/5005—Allocation of resources, e.g. of the central processing unit [CPU] to service a request
    • G06F9/5027—Allocation of resources, e.g. of the central processing unit [CPU] to service a request the resource being a machine, e.g. CPUs, Servers, Terminals
    • G06F9/5038—Allocation of resources, e.g. of the central processing unit [CPU] to service a request the resource being a machine, e.g. CPUs, Servers, Terminals considering the execution order of a plurality of tasks, e.g. taking priority or time dependency constraints into consideration
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N5/00—Computing arrangements using knowledge-based models
    • G06N5/02—Knowledge representation; Symbolic representation
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F2209/00—Indexing scheme relating to G06F9/00
    • G06F2209/50—Indexing scheme relating to G06F9/50
    • G06F2209/5011—Pool

Definitions

  • the subject matter described herein relates to systems, methods, and computer program products for automated and generative multimodel multiclass classification and similarity analysis using machine learning.
  • This current subject matter is directed to a process in which computers can be used to efficiently create classifications and similarity analysis using probabilistic models generated through the principles of machine learning.
  • the process does this in an automated way via use of generative models in which samples further train the system and result in iteratively better models that correctly represent the best predictive capabilities expressed within a particular sample population.
  • the process can be defined by five major functionality components and the infrastructure required in order to support these functions.
  • a sample is any piece of data that you wish to classify or perform similarity analysis against similar samples.
  • a feature is any salient data point that the system measures from a sample.
  • a model is a single or multimodel probability matrix that defines the likelihood of any sample to be classified in a particular class.
  • a multiclass classifier is one that can support classification in two or more classes.
  • a multimodel classifier is one that uses sub models to handle particular intricacies in a complex sample.
  • a generative classifier is one in which samples used to classify may become training material for future analysis.
  • a sample of data is placed within a directed graph (e.g., a directed acyclic graph).
  • the directed graph comprises a plurality of hierarchical nodes that form a queue of work items for a particular worker class that is used to process the sample of data.
  • Work items are scheduled within the queue for each of a plurality of workers by traversing the nodes of the directed graph. Thereafter, the work items are served to the workers according to the queue. Results are then received from the workers for the work items.
  • the nodes of the directed graph are traversed based on the received results.
  • the results can include extracted features from the sample of data.
  • the samples of data and/or extracted features in some cases can be classified.
  • at least one model e.g., machine learning model, etc.
  • the sample of data can be used to simply classify the results.
  • Data characterizing the classification can be provided in various fashions such as displaying the data, loading the data into memory, storing the data, and transmitting the data to a remote computing system.
  • the results can include routing data that is used to determine where to schedule a next subsequent work item in the queue.
  • An order of each sample can be prioritized prior to adding such samples to the queue so that each sample is added to the queue according to the prioritized order.
  • the priorities can be based on a pre-defined rate of processing.
  • Prioritization of the at least one sample can be locally adjusted in real-time.
  • the work items can be scheduled in the queue according to at least one of sample prioritization or worker rate.
  • the workers to which the work items are served can be part of a pool having a dynamically changing size based on available resources.
  • the available resources can be based on determined supply and demand.
  • the sample of data includes files for access or execution by a computing system, and wherein the classification indicates whether or not at least one file likely comprises malicious code.
  • the sample of data includes medical imaging data and wherein the classification indicated whether or not at least one portion of the medical imaging data indicates a likelihood of an abnormal condition (e.g., cancerous cells, etc.).
  • Computer program products are also described that comprise non-transitory computer readable media storing instructions, which when executed one or more data processors of one or more computing systems, causes at least one data processor to perform operations herein.
  • computer systems are also described that may include one or more data processors and a memory coupled to the one or more data processors. The memory may temporarily or permanently store instructions that cause at least one processor to perform one or more of the operations described herein.
  • methods can be implemented by one or more data processors either within a single computing system or distributed among two or more computing systems.
  • Such computing systems can be connected and can exchange data and/or commands or other instructions or the like via one or more connections, including but not limited to a connection over a network (e.g. the Internet, a wireless wide area network, a local area network, a wide area network, a wired network, or the like), via a direct connection between one or more of the multiple computing systems, etc.
  • a network e.g. the Internet, a wireless wide area network, a local area network,
  • the current subject matter described herein provides many technical advantages. For example, the current subject matter provides for the automatic generation of models thereby obviating the need for human generated models and the errors associated therewith. Furthermore, the current subject matter provides enhanced techniques for classifying data/files for various applications.
  • FIG. 1 is a system diagram illustrating major components of a system for implementing the current subject matter
  • FIG. 2 is a diagram illustrating a process of using a query interface
  • FIG. 3 is a diagram illustrating a process for active collection
  • FIG. 4 is a diagram illustrating a process for a passive collector interface API
  • FIG. 5 is a diagram illustrating a process of extracting features
  • FIG. 6 is a diagram illustrating a process of model generation
  • FIG. 7 is a diagram illustrating a process for generative multimodel multiclass classification and similarity analysis.
  • the system described herein can make use of key underlying infrastructure to provide its services, namely to extract features from a sample of data, make classifications regarding some or all of such extracted features, and generate machine learning models using such data that can be used to characterize subsequently data samples.
  • the infrastructure can include a unified data access layer 135 that access a cache 140 that stores data from data sources such as relational data stores 145 and big data sources 150 .
  • the infrastructure can include a router 155 , a scheduler 160 , and a resource manager 165 .
  • the components of the infrastructure can be implemented in software, hardware, or a combination of both.
  • the infrastructure is shared among the functions and acts as glue that binds the functions together. It has a key focus on optimization of the overarching process. It facilitates data flowing through the system, determines which resources are required, and appropriately schedules an optimal pathing and resource profile for the data.
  • An external source 105 e.g., via a web service and/or other network, etc.
  • An external source 105 can query data from a query interface 110 and/or submit data to a collection interface 115 .
  • Various features from data obtained from an interface can be extracted by an extraction component 120 .
  • the extracted data can be classified (or otherwise characterized) by a classifier component 125 .
  • a model generation component 130 can generate or otherwise use one or more models (e.g., machine learning models, etc.) based on the extracted and/or classified data.
  • the workflow can use a centralized but scalable routing and prioritization system. Individual workers register with the central resource manager 165 and receive work via a pull mechanism. In addition, the workflow system can support a high degree of concurrency and flexibility. Work items can be heavy or light weight and will be scheduled to appropriate workers.
  • the workflow system can use a system to allow workers of each particular type to be added and removed without restarting the system. It also allows new ordering to be implemented and new types of workers to be added and existing workers to be removed.
  • the optimal workflow can be represented as a directed graph such as a directed acyclic graph (DAG), in which nodes are represented as the individual classes of workers. While the current subject matter uses DAGs as an example, other types of hierarchical arrangements/directed graphs can be utilized.
  • the graph is defined by worker class prerequisites in a backward chain dependency mapping process that attempts to generate the shortest path through the graph. This process results in a forward chaining of worker classes that represent the optimal pathing through the workflow.
  • individual worker classes can define prerequisites via a configuration file.
  • This configuration contains a seed of a routing system, allowing an optimized route to be built up at runtime.
  • individual worker classes can be built into a DAG which can then be published to, for example, a central resource manager 165 .
  • the central resource manager 165 can then use this new DAG to determine optimal routes for traversing a particular sample of data (which results in selective scheduling of work items to workers/classes of workers).
  • the central resource manager 165 using the router 155 can define where work items go next. This frees individual workers from having to know or care how messages are passed.
  • the central resource manager 165 relies on the configured graph paired with the output of each worker to define the optimal next step in the workflow process.
  • a sample of data can be placed in a state in the DAG, represented as a queue of work items for a particular worker class.
  • a worker can be served the work item by the router 155 .
  • the worker can then return the work item and, in some cases, data useful for routing such as additional layers of information to assist in routing. For instance, if a worker determines that the object has shown signs of corruption, flagging this can help route the object to a better solution.
  • the central manger can now determine the optimal next route based on the results of the current work item and the results of the current worker.
  • the central resource manager 165 then shifts the sample to the next state as defined by the DAG.
  • the central resource manager 165 using the scheduler 160 can be responsible for scheduling work for individual workers.
  • the central resource manager 165 can do this by managing the ordering of work items in the queue.
  • individual items can be provided to workers based on a variety of factors including sample prioritization and worker rate.
  • the scheduler 160 is responsible for handling work items in exceptional cases, such as those being handled by workers who have gone offline or are operating out of parameters.
  • a sample can enter a particular DAG state.
  • the scheduler 160 can them determine the order of the sample in the queue.
  • the scheduler 160 can watch for work items that have been in a checked out state for too long and reinsert them in the waiting state at the appropriate place.
  • Prioritization offers both global and local optimization potential. Local prioritization optimization focuses on operations within a node in the system, where global optimizations involve prioritization operations that affect multiple hosts and route paths. A higher priority sample gets precedence over lower priority samples. Prioritization of a sample can be locally adjusted in real-time to accommodate exceptional scenarios. Higher priority can be reserved for sample processing that require a shorter time to complete analysis. Lower priority can be reserved for bulk processing from passive sources such as backfill operations or samples read from feeds.
  • individual samples can be assigned a prioritization based on the mechanism of insertion into the workflow.
  • the scheduler 160 can use this global prioritization throughout the workflow, giving higher priority work items shorter queue wait times, and lower priority work items longer wait times.
  • the scheduler 160 can determine that the current prioritization for a work item is incorrect or causing a block in the queue. In such cases, the scheduler 160 can dynamically adjust the prioritization for the work item higher or lower depending on the scenario.
  • the infrastructure can provide a dynamic scaling factor based on a number of criteria.
  • the primary global metric for scaling can be total processing time for a work item.
  • a number of local metrics can be considered and adapted to. These local metrics can include, for example: average sample processing time per activity, individual activity work factor, per host load metrics, and available RAM, CPU, and disk.
  • the overall system can ensure continuous operation of the whole system in the event of small or large localized failures.
  • the scheduler 160 can provide a failsafe self-healing mechanism.
  • the dynamic scaling can be achieved via a combination of detailed monitoring and rapid adaptation of the available resource pool (i.e., the pool of workers, etc.), as well as optionally scaling the complete pool of resources. This process can be managed by the central resource manager 165 .
  • the resource management system as implemented by the resource manager 165 can utilize the concept of resource pools.
  • a resource pool is a representation of all computer resources available within a particular class. Classes can generally be defined on commonalities such as operating system, or services offered. Each pool can be divided into resources given to individual workers.
  • Resource pools via the resource manager 165 can grow or shrink based on need and resource availability and cost. When resources are readily available and low cost, the resource manager 165 can grow the overall pool and service higher volumes. When resources become scare or costly, the resource manager 165 can shrink the overall pool and adjust the overall rate to suit requirements.
  • the resource management system as implemented by the resource manager 165 can utilize a variety of externally measured and self-reported feedback metrics that help determine resource pool utilization rates, and perceived demand.
  • measured metrics are those collected by a metric and statistics collection system. They tend to focus on the hard asset metrics such as CPU utilization, available memory or disk space. These are useful to help the resource manager 165 determine resource pool usage metrics, and define under- or over-utilized resources in the pool and adjust the load appropriately.
  • self-reported metrics are useful for determining deeper level of details. The workers and the resource manager 165 report data on processing rates and task times that allow us to determine highly granular resource usage. They are also useful for determining intended demand for resources.
  • the resource manager 165 can determine what is the root cause and what is the symptom, allowing the system to focus our resource optimization on the root cause and alleviate the symptom that way.
  • a particularly important set of self-reported metrics comes from the scheduler 160 .
  • the scheduler 160 can track individual worker rates of sample processing and calculate aggregate averages for rates (measured supply).
  • the resource manager 165 also knows the size and wait times for each DAG state work queue (measured demand). These metrics are particularly useful for calculating anticipated total processing time, and determining if there are backups in the workflow.
  • the scheduler 160 can report these metrics to the resource manager 165 .
  • a corresponding metric is sent by the node generating the event to the resource management system.
  • the resource manager 165 can then create time series based aggregate and individualized measurements based on the self-reported data. These time series data points can be graphed and measured against known baselines. When time series data goes beyond certain points an action can be triggered, such as deploying additional resources into the resource pool.
  • the resource manager 165 can utilize various systems to monitor external states of resources including resource utilization rates as well as availability of resources. These measurements can help determine the optimality of usage. External monitoring can be conducted on a peer basis and also by using dedicated monitoring systems. These systems can continuously poll their intended monitoring targets (such as a resource that cannot self-report its state) and report the results to the resource manager 165 .
  • a resource monitor can reach out to a target system.
  • the resource monitor can then report the results of the monitoring action to the resource manager 165 .
  • the resource manager 165 can determine an optimal deployment of resources. It can ask individual workers to reconfigure themselves to suit the intended demand.
  • the resource manager 165 can operates on a timed resource leveling cycle. Once per timed period (say 5 minutes), the amount of used resources is compared to the amount of available resources. Any differences in trends of used and available resources should trigger appropriate remediation such as adding or removing resources from the pool. As reconfiguration of resources does incur overhead, it is imperative that the effect of reconfiguration be managed by the resource manager 165 to ensure the overall system is gaining a net benefit. This cycle can be designed to avoid undue constriction and dilation of resources leading to “flapping”, where resources are continuously being reallocated for whichever task is currently the highest demand.
  • the resource manager 165 can also adjust the size of the pool based on not only existing supply and demand, but also external factors like availability and cost.
  • the resource manager 165 can use a rules-based approach to appropriately weight each metric in its calculations.
  • the rules can be dynamically configurable but measured and refined based on the existence of available metrics and their value to the overall calculation.
  • the resource manager 165 can isolate a set of appropriate metrics collected during the cycle period under observation. The set can be defined based on a rules based approach. The resource manager 165 can then determine supply and/or demand from its set of metrics. The resource manager 165 can then examine the current resource pool allocation in light of the existing supply and demand. Next, the resource manager 165 can calculate a new resource pool allocation based on its calculations of supply and demand. In addition, the resource manager 165 can reconfigure workers by asking existing workers to perform more necessary tasks, by adding new workers, or by removing workers altogether, to match the new resource pool allocation. The new allocation pool can then be saved for use by the resource manager 165 /scheduler 160 /router 155 for a next period.
  • the resource manager 165 can determine if the current resource pool allocation is too tight or too slack based on a threshold value (e.g., if 80% of workers are busy, >90% of the time, add more workers; or if ⁇ 10% of workers are busy ⁇ 50% of the time, remove workers from the pool, etc.). It is too tight if demand is outpacing supply. It is too slack if supply is outpacing demand. If the resource pool is too slack, the resource manager 165 can determine an optimal value to reduce the resource pool. The resource manager 165 can reallocate a new resource allocation for the reduced pool and engage the workers to reconfigure based on this new allocation.
  • a threshold value e.g., if 80% of workers are busy, >90% of the time, add more workers; or if ⁇ 10% of workers are busy ⁇ 50% of the time, remove workers from the pool, etc.
  • the resource manager 165 can examine external cost and availability of additional of resources. Next, the resource manager 165 can choose to increase resource pool size, or decrease incoming volume by rate limiting. Rate limiting happens when a global processing time metric is being met. Resource pool increases happen when they are not. Further, the resource manager 165 can reallocate a new resource allocation for the increased pool and engage to reconfigure based on this new allocation.
  • the resource manager 165 can also serve the purpose of ensuring operation in adverse conditions.
  • the system can be designed to presume that individual workers are temporal and may appear and disappear both under the control of the resource manager 165 and due to external factors.
  • the resource manager 165 can utilize its monitoring system to address problems that arise in the operations.
  • problems can be defined by a specific set of rules applied to metrics.
  • the metrics are typically external in nature, as an internally reported metric may not be available if the system that reports that metric has a fatal situation.
  • the resource manager 165 can decommission continually underperforming assets in the resource pool and replace them with better performing assets. It can also raise alerts for manual intervention in the event that automated responses are not effective in alleviating the problems.
  • Self-healing can be implemented by having the resource manager 165 examine the current resource pool for underperforming and non-responsive assets. Thereafter, the resource manager 165 can create a new allocation using existing pool resources or scale the pool larger to replace those resources that are failing. The resource manager 165 can ask the new resources to configure themselves to replace the old resources. The resource manager 165 can later decommission old resources.
  • the current system utilizes several types of data stores which can be accessed by the unified data access layer 135 .
  • Various elements of the overall system have different requirements for data consistency, persistence, performance, and integrity.
  • the system can utilize elements of large document based stores (e.g., big data 150 ), relational stores 145 , and persistent and temporal caches 140 . To achieve scale and resilience, all of these systems can utilize redundancy and horizontal sharding of data.
  • the unified data access layer 135 can have various needs for security levels of the data, as not every element needs complete and unrestricted access to all of the data.
  • the infrastructure can also provide a system to unify access to the resources. It can allow each of the systems to provision its particular needs, and it matches these needs with an appropriate backend store. It offers the system the ability to manage access and monitor usage for optimal access. Additionally it can offer an abstraction layer that reduces the requirements needed to implement a worker process.
  • the workers and various elements of the system access the unified data access layer 135 through a specified API.
  • the unified data access layer 135 can use a template driven system to allow for rapid mapping of API calls to underlying data sources.
  • the API can be primarily REST based and it can support SSL and multiple forms of authentication.
  • the data access manager can support addition and removal of API interfaces, handle management of back-end data resources, and manage security access to these interfaces via the supported mechanisms.
  • a particular need for a data access API can be defined. Parameters can be defined for consistency, performance, and persistence. Requirements regarding security and confidentiality can also be defined.
  • the API manager can then provision appropriate back end resources to make them available. Thereafter, the API manager can link the back end resources with a front end REST call, with an optional translation operation.
  • the component in question does a service lookup request for the data access API service. Once located, the component can query the API and determine which services it offers.
  • the service definition can include details on parameterization of access as well as security mechanisms required to access those services and serves as a guide for a component to access an API.
  • the component knows where a service is located, and how to access it, it can make calls to the data access API as it wishes. In the event of a failover, a redundant peer can serve requests.
  • the unified data access layer 135 can support multiple back end solutions. This arrangement helps developers to avoid worrying about access to multiple data sources, using multiple confusing drivers that may or may not be available on the platform.
  • the back-end systems are defined by a set of conditions available to them such as resilience, performance, persistence, consistency, and cost.
  • Resilience can be defined as the ability to continue operations under stress. Any backend component should be able to lose various parts of its operational capacity and be able to adapt effectively to adjust load across the remaining parts. There may be some degree of data loss as a negotiated requirement.
  • Performance can be defined by the speed at which data can be delivered or written. Some particularly complex data access operations can be long running and immediate response is not a requirement. Others require specific response times to ensure the viability of the application.
  • Persistence can be defined as the amount of time and size of the required data that must be kept in a recoverable state. The period could be from minutes to forever, and the data size requirements are necessary to help properly plan and scale the back-end data stores.
  • Consistency can be defined as the assurance that all nodes in a service grouping give the exact same result. This can be affected by certain clustering and replication actions as well as network distance between the clustered nodes. A system with no consistency does not bother with ensuring all nodes are the same. One with high consistency attempts to ensure that all nodes have the same data in near real-time. A common practice is one of “eventual consistency”, in which the system will reach a defined equilibrium of consistency but makes no guarantee on the defined period.
  • the query interface 110 is a general component of the overall system that allows external entities to ask questions in regard to samples that have already been processed.
  • the query interface 110 serves as the primary point of contact between external systems 105 and the accumulated knowledge of the system. It is intended to answer questions in a highly efficient manner.
  • the query interface 110 only answers questions for which it has existing answers. Those questions for which the query interface 110 does not have existing answers are passed to the collection interface 115 for further analysis.
  • the query interface 110 can utilize a REST API. This allows it to utilize session encryption via SSL and offer a variety of authentication options.
  • the query interface 110 can be designed to answer questions based on specific acceptable metadata regarding the sample. This allows questions to be asked without requiring a transfer of the full sample. This can be helpful when the sample is large, complex, or not directly available.
  • the query interface 110 can be a specialized layer on top of the unified data access layer 135 .
  • the query interface 110 can interpret external requests via the API into queries against the unified data access layer 135 . For those elements that it cannot answer via this access, the query interface 110 can provide details for the client to access the collection interface 115 to provide a path to get additional information.
  • FIG. 2 shows a process flow diagram 200 related to the query interface 110 .
  • an external developer can determine they require the ability to query a specific sample set. The developer can then define the specific metadata required for access.
  • the query interface 110 can implement an appropriate set of unified data access layer views into its data stores.
  • the query interface 110 documentation can inform the developer of the specifics of the query interface API endpoint for the sample set they require. It also provides appropriate details in regard to required authentication and encryption.
  • the developer can implement the appropriate query interface API within their client.
  • the client can send the appropriate metadata comprising a question.
  • the query interface 110 can check the unified data access layer 135 and, at 230 , responds with the answers, or if answers are unavailable, at 235 respond with appropriate details on how to access the collection interface 115 (i.e., ask for a submission, etc.).
  • the collection framework components including the collection interface 115 can allow a defined path to get samples of data into the system for analysis and growing our knowledge. These samples serve as bits of information that offer immediate value based on the highest configured model as a classification. They also offer similarity analysis capability, allowing a submitted sample to be put into context among existing samples.
  • the collection interface 115 complements the query interface 110 as a primary external conduit for input into the system.
  • the collection interface 115 can allow systems to be interfaced to provide samples for input and receive responses as output.
  • the collection interface 115 can do this via a series of REST API interfaces. These API interfaces can be either public or private, and support SSL and authentication of various sorts.
  • Samples can be gathered in one of two general ways. Samples may be “pulled into the system” using an active collection mechanism defined within the system. Alternatively, they may be “pushed” to the system via any collection interface API compatible solution. The combination of active and passive collection allows the system to harvest samples and integrate into existing products in a very light touch way.
  • the collection interface 115 can make use of mechanisms to reduce the incidence of unintentional reprocessing.
  • the collection interface 115 can do this by offering basic and complex caching of existing results, and a submission mechanism that can be adjusted to accept or reject submissions based on rules such as current existence and perceived fitness of the sample.
  • the collection interface 115 can integrate into the resource manager 165 provided by the general infrastructure. From this integration, the collection interface 115 can adjust rates of input to meet goals, and can offer a prioritization of sample submissions giving precedence on a variety of factors such as preconfigured API priority or dynamic rate adjustment in active collection. This offers a fundamental method to allow for dynamic optimization of submission rates utilizing whole system feedback. It can also help alleviate pressure on existing resource pools in lieu of growing or shrinking existing resource pools. When a sample is collected either passively or actively, the collection interface 115 can push the sample into the appropriate workflow system for processing.
  • Active collection offers a process in which the collection framework can manage actively gathering samples for submission. It can offer the ability to manage and schedule collection via the collection interface 115 . It adds the benefit of using feedback from the existing resource manager 165 to dynamically adjust rate if desired.
  • An active collector is a subsystem that does some action on a specific period.
  • the active collection framework can manage the rules that define what an active collector may do. Additionally, it can define a mechanism that works with the existing infrastructure scheduling system to support periodic execution of the collectors. This period can be defined as once per timeframe (ex: every 30 minutes), once per clock/calendar time (ex: 1 PM every Monday), or in a continuous manner.
  • the scheduler 160 can ensure appropriate tasks are issued to workers on the correct period.
  • Collectors can allow internal code to be run on the period, external programs to be run, or even make API calls on the period.
  • Active collectors generally do not interface with the query interface 110 ; rather they can access the unified data access layer 135 directly. Because these requests do not originate external to the system, they generally have less need for knowing about the current state of a sample and the associated knowledge the system has of that sample.
  • FIG. 3 is a diagram 300 illustrating a process for an active collector.
  • a developer can define and build an active collection method.
  • the collection framework can require rules to be written that define the period of active collection (either once per time range, once per set repeated fixed time, or continuous) which the developer, at 310 , can provide.
  • the developer can set rules of collection such as how the active collection is gathered.
  • the collection framework can manage the scheduler 160 to introduce the task (i.e., apply the active collector) at the appropriate period.
  • the scheduler performs the required logic for the active collector on the appropriate period.
  • One type of active collection can utilize a web crawling infrastructure.
  • This web crawling infrastructure can allow a general or specialized web crawl to attempt to find suitable samples on the web for analysis.
  • the general web crawler can process HTML pages in a standard manner. It parses the HTML and attempts to locate links and resources that would make appropriate samples, and pushes those samples into the system.
  • a specialized web crawl can be one whose scope is limited, and uses specific techniques to make the scan more appropriate. For instance, a specialized web scan can be performed against a specific site, tailored to that site. It can incorporate site-specific details such as previously gathered information to make the query more effective.
  • Another type of active collection can involve running arbitrarily complex queries either in the infrastructure's unified data access layer 135 or in any available external data store. Like all active collection mechanisms, this happens on a specific period.
  • the active data store collector can reach out to the appropriate data store and collect data to be processed.
  • the active data store can do this individually or via a bulk query.
  • Passive collection is a subsystem of the collections interface 115 that can facilitate allowing external access to submitting new samples in an on-demand manner. Generally, it can be used in conjunction to the query interface 110 . Passive collection can provide a specific API that allows samples to be submitted. This API can be generally useful for when existing knowledge of a sample is missing, incomplete, or otherwise unavailable. Such an API can be a REST API that, in turn, enables SSL for session encryption, and offers a variety of authentication mechanisms to ensure desired levels of authentication and access control.
  • the passive collection interface API can generally operate in a three-stage manner. First, the passive collection interface API can confirm that the system is ready to accept a submission of a sample. If the system desires the sample, it provides details on where to put the sample. Finally, it expects a confirmation that the sample is put in the proper place.
  • the above system allows for a configurable method of upload.
  • the original response from the passive collection interface API defines where the sample should be uploaded, along with any constraints or caveats.
  • This can be a simple HTTP upload, a reference to an FTP or other publicly available URI, or an entirely other system as defined by the sample type and specifics of this instance of the passive collection interface API. This allows the process of uploading to be properly offloaded, and abstracted to best suit multiple types of samples.
  • the developer can implement the collection submission client API within their existing product or system. This implementation requires configuration on which submission API endpoint to use, and any credentials if required. During normal product operations, submissions may be generated. Each submission can be sent to the collection submission API. The collection submission API can determine classification and similarity analysis based on current knowledge of the sample and return those results. If additional details are required for analysis, the submission API can offer additional functions to request these details. Failure to provide these details on behalf of the developer may result in lower confidence scores for classification and similarity analysis.
  • FIG. 4 is a diagram 400 for accessing the passive collection API.
  • the query interface first provides, at 405 , details on the passive collection API to the client. Thereafter, at 410 , an external client entity asks to submit a sample (i.e., requests a submission to the passive collection interface API. This can be paired internally with the query interface API.
  • the passive collection interface system can determine if it desires a submission of the sample based on specifically designated criteria (previous existence, age of existing knowledge, etc.). If the passive collection interface system desires the sample, it responds to the external client entity with an affirmative response and details on where to “put” the sample, otherwise, at 420 , the client receives a negative response.
  • the external client entity can perform, at 425 , the necessary actions to “put” the sample in the correct place.
  • the external client entity can then submit, at 430 , a confirmation to the passive collection interface API.
  • the passive collection interface system can, at 435 , place the sample in the proper workflow for further processing.
  • the extraction component 120 can serve the purpose of converting samples into salient data points, or “features” used for classification and similarity analysis.
  • the extraction component 120 can do this by using a highly scalable, parallel processing distributed system to extract this information.
  • the extraction component can be defined by a series of worker classes handled through the infrastructure dynamic workflow system. These worker classes can be developed on a per-sample type basis and allow the system to grow in a scalable manner. These worker classes can be effectively chained together by the DAG router 155 in the workflow system to ensure that additional extraction workers can be added and removed at runtime in a dynamic manner.
  • Each worker class can represent some set of extracted features and can be associated with a specific sample type.
  • the system can use dynamic typing of samples to ensure that the correct operations are performed on a per sample basis. It achieves this via the use of the infrastructure router 155 and scheduler 160 .
  • the router 155 can ensure that samples are processed in an appropriate manner.
  • the scheduler 160 can ensure that the samples are processed in a timely manner.
  • the resource manager 165 can ensure that there are adequate resources available to perform each class of work in an on-demand and planned basis.
  • Each worker class represents a resource that needs to be managed by the resource manager 165 .
  • the worker class registers with the router 155 and other infrastructure systems to help the system understand the capabilities it has on hand.
  • a worker class registers it provides a set of features it can provide to the router 155 , as well as a set of routes and associated prerequisites and conditionals for each route.
  • Each route can either be a default route (one taken if no other route has been selected), or a named route.
  • Each named route has a series of conditions that must be met to be selected. If these conditions are met (based on the features accumulated for a sample), this named route is selected.
  • the extraction component 120 can use a dynamic set of routing conditions that are dynamically defined in process. As a sample navigates the workflow as assigned by the router 155 , the extraction component 120 can add additional information to the sample profile, allowing for more specialized worker class operations to be performed that produce deeper details.
  • FIG. 5 is a diagram 500 illustrating extraction of features.
  • the router 155 can place a work item in a worker queue. Each work item can comprise one or more tasks to effect extraction. Subsequently, at 510 , the worker receives a work item from the worker queue and, at 515 , processes it. If the task was successfully processed, it is determined, at 520 , whether there are steps/tasks to perform. If the task was not successfully processed, the work item (or a portion thereof) can be placed back in the worker queue and the process repeats. If it is determined that there are no new steps (at 520 ), then, at 525 , extraction is finished, otherwise the router 155 sends the work item to the next step.
  • a task is a bit of work performed on a sample, a step is a route to the next task to be performed. In graph theory, a task would be considered a node (or vertex), while a route would be an edge.
  • This process can occur with a dynamic typing system that adjusts based on previous knowledge collected in process.
  • the existence of this knowledge is one of the factors used to create the reverse chain Directed Acyclic Graph used by the router. This ensures that the information required to make a decision at any point in the workflow is available when needed.
  • This arrangement generally defines a set of prerequisites and conditional factors on a per work class basis.
  • the prerequisites define those data points that need to exist before the routing decision can be made by the router 155
  • the conditionals are those features that are salient to the routing decision. Additionally, conditionals and can be performed on non-feature based data, such as an external API call.
  • a dynamic type-based route can be specified by a developer first defining those conditions that are required to perform the appropriate decision. Thereafter, the developer can determine which features can provide the data needed to meet the conditionals and mark these as prerequisites. The developer later can define the features this work class will provide. The router logic system can then calculate a Directed Acyclic Graph based on the required prerequisites, conditionals, and provided data points. The router logic system can build optimal models for the workflow based on this data.
  • Worker classes can form emergent “chains” of operations based on the Directed Acyclic Graph built and maintained by the router 155 .
  • the chain can define the operations required to optimally extract all available features from a particular sample. While the workflow is defined in process, some chains consistently match certain types of samples.
  • a chain can be locally optimized to reduce the number and types of transitions.
  • the resource manager 165 can do this by allocating partial and whole chains to a single compute resource, allowing operations to happen in an effective manner with minimal transfer time.
  • This arrangement can reduce the overhead involved in forcing data to be moved around in a parallel-distributed system, greatly enhancing the throughput the system provides.
  • This arrangement can also allow for the creation of specific enclaves of processing, which is useful if a particular sample type has an anomalous usage structure (i.e. it uses a significant amount or resources or takes a long time to process).
  • Chains can be continually measured and optimized. This arrangement can allow for both automatic and manual adjustments based on ongoing feedback from the system. Knowledge of how chains perform offers the resource manager 165 better knowledge to optimize and anticipate resource utilization based on probability of anticipated chain usage.
  • the resource manager 165 can also utilize real-time chain metrics to define if specific chains are over- or under-performing and adjust its global resource usage allocation appropriately. Chains that are infrequently used can be reduced and have resources assigned to those chains that are in heavy use.
  • New chains can be continually added to both consider new sample types as well as add additional depth to existing sample types. With this, the overall system gains new sources of information available to refine and consider new models for classification and similarity analysis of samples.
  • the classifier component 125 can provide a method to classify features extracted from a sample against existing models.
  • the classifier component 125 can also provide similarity analysis across defined similarity points.
  • the classifier component 125 can operate as independent worker classes focused on creating classifications.
  • the classifier component 125 can create emergent chains within a Directed Acyclic Graph that the router 155 can use to push samples through via routing decisions.
  • these chains can be specific to sample types, allowing for a large amount of specialization in classification. Some of this specialization will be in regard to specific feature sets associated with individual samples.
  • the classification chain can comprise a set of workers that operate similarly to the extraction workers, or analysis workers. Their purpose can be to specifically create classifications and perform additional classification logic based on internal data like features and external data like data from exterior classification systems. Generally, the internal classifications can be based in part or whole on the output of specialized classifiers embodying the machine learning algorithms. Even those that do not utilize machine learning directly benefit from it, as it is used to measure optimal aggregation algorithms to best represent the current state of knowledge of the model.
  • the classifier component 125 can utilize the router 155 and scheduler 160 and abide by all of the inherent aspects of prioritization and optimization of routes. Additionally, the classifier component 125 can operate within the resource manager 165 , allowing rates to be adjusted in real-time by allocating more resources to the classifier worker classes.
  • Similarity analysis can be performed using analytics models trained against a representative sampling of the population of all samples. Similar chains can handle large scale directed and undirected similarity analysis. Generally, directed similarity analysis can offer a higher degree of useful output. Undirected similarity analysis tend to be used more to explore sameness against the population, and to attempt to identify those samples that are fundamentally different from anything previously seen. Each similarity analysis can be time and model dependent. Having a new model or a different set of samples in the population may cause the similarity analysis to be recalculated.
  • Similarity analysis can further allow the detection of suitably anomalous “outliers”. These outliers can be useful to determine that the models in question are still actively in range. If the rate of outlier detection goes up, the models being used can be re-evaluated to incorporate the newest types of samples within the population.
  • Classification can be conducted by the router 155 deciding that a sample is ready for classification when all requisite elements have been collected or loaded from a previous data store. Thereafter, the router 155 can shift the sample into the head of an appropriate classifier chain. The chain can then be executed and a final classification score can be calculated. The classifier chain can interface with the unified data access layer 135 so that the results of the classification can be stored. The classification of the sample is now available for future queries and usage in other places.
  • Similarity analysis can be conducted by the router 155 deciding that a sample is ready for similarity analysis when all requisite elements have been collected or loaded from a previous data store. The router 155 then shifts the sample into the head of an appropriate similarity analysis chain. The chain can then be executed and sets of similarity analysis scores can be calculated. Undirected similarity analysis can be stored in an area reviewable by humans to infer meaning. The similarity analysis chain can interface with the unified data access layer 135 so that the results of the directed similarity analyses can be stored. The similarity of the sample can then be available for future queries and usage in other places.
  • the classifier work items can support the use of aggregate classifications. These types of classifications can be layers built on top of previously calculated classifications. In a first pass classification, a sample can receive several individualized classification scores. A second stage can allow additional logic to be defined to best calculate effective aggregate classification scores to suit a particular need.
  • the use of a multi-stage classifier can allow complete and partial reclassification at high velocity and volume with minimal resource utilization.
  • missing elements in the multi-stage classifier can be backfilled if they are missing, incomplete, or not up to date. These calculations can be performed during processing, offering the use of the latest and most complete picture on each classification decision.
  • the system can use models to answer classification and similarity analysis questions about samples.
  • models that drive the classifiers can be defined, developed and built. For these models to be effective, the cost of model generation needs to be minimized and optimized for cost and accuracy.
  • a model can comprise a probability matrix based on machine learning techniques. To do this, a training set of samples can be extracted from a sample population and used to generate a model (i.e., a model can be trained using historical data in order to characterize future data sets/samples, etc.). The model can then be back-tested against a large validation set (that does not contain the sample set). Once a model is judged valid, it can be put into production use for the classifiers.
  • a subset of the total population can, in most cases, be used because the samples have a reasonably standard distribution. Because of this, using a large enough total population, and a large enough subset of that for training picked at random, results in a fair representation of the samples in total. Due to ongoing submissions of samples into the system, the number of sample sets and training sets can continuously increase. This arrangement allows iterative models to be built that have enough density to allow sub models to be built to perform better analysis in specialized classifiers.
  • the model generation system can be designed to be hands off, allowing the use of internal measures to attempt to create optimal models. This involves defining success and failure criteria and finding the way to measure and compare the fitness of models to a particular task.
  • FIG. 6 is a diagram 600 that illustrates a process for general model generation.
  • a complete census of the sample population can be taken (which can be referred to as the “whole population”).
  • a random subset can be selected to act as a training set.
  • a second random subset of the population can be selected from the remaining whole to serve as a testing set.
  • a model can be generated by applying machine learning classification techniques to the training set.
  • the training set is processed into large vectors of numbers, and these vectors are used in a variety of machine learning algorithms including logistical regression, neural networks, support vector machines, and decision tree ensembles with custom tuned variables to generate a series of models.
  • the models are further tested and refined to produce end stage models for use in the general system. Thereafter, at 625 , the generated model can be tested against the testing set and, optionally, confidence intervals can be calculated. The confidence intervals can be compared against existing models to judge fitness and make a determination whether the model failed which causes 610 - 625 to be repeated or whether to publish the model (at 630 ).
  • the training set generator can be configured to attempt to reduce any specific bias in the selection of samples for the training set from the general sample population.
  • the training set generator can do this by randomly selecting training samples from the population.
  • Sub models presuppose a type of bias when compared against the general set, and so it is important that sub models only be compared against those appropriate samples for which they can be representative. This is because a sub model is created against a subset of the entire population. To generate sub models, it is important to apply certain criteria as a primary filter. As long as this primary filter is applied uniformly, both for sample selection and for future classification, and samples are chosen at random from the post filter results, there is no additional bias introduced, at least within the scope of items pertaining to the sub model.
  • an individual training set may not be entirely representative.
  • the system can attempt to increase the training set size and run multiple iterations to ensure that the variance of a particular model is not unduly biased.
  • the iterative models can add additional weightings to favored features based on appropriateness. By increasing the number of iterations, confidence of the validity of the weights of a feature set can be gained, resulting in a stronger overall classifier.
  • Models can use some subset of available features to generate classification models. Because the number of salient features can become rather large over time, and the training sets may become rather large, there may be a desire to reduce the set of features considered in an attempt to optimize the generation process.
  • the process of pruning features can be done in an iterative manner. By reducing the set of features to those that positively affect the model, it can drastically reduce overhead in classification. Each iteration, those features that provide weak scoring (as based on a fitness function) can be dropped in subsequent iterations. This is an optional step, as full models may be more accurate than pruned/reduced models.
  • the set of observed features for a sample can be a subset of the overall feature space, due to either the absence of a subset of features from a sample, or the inability to observe a complete set of features.
  • the system can, for instance, generate estimated features for those features not in a sample subset, define absent features as valid features for comparison, or utilize a multimodel approach to compare samples at an abstract level, where feature spaces are not directly compared.
  • models can be effectively trained to use any form of supervised learning. To do this, a set of data for which you know or suspect a classification at a high confidence must be compiled. This training can be done by various methodologies. It can be hand validated, or machine generated in some contexts.
  • Unsupervised machine learning does not require training, but can be much more variable in its output. If the system is designed to be exploratory in nature, the training set can be judged to be the entire set.
  • Similarity analysis does not necessarily require training. It can take advantage of a previously classified sample to help categorize the similarities in question and judge fitness of those samples, but even this is not required. Classification offers the ability to layer context on top of similarities that may be detected, versus having to determine context from observed similarities.
  • Models can be validated based on a level of fitness.
  • Fitness in this sense is the accuracy of the model, along with other facts.
  • the system can allow for the definition of a set of criteria that defines fitness. It can be used as a low-water mark to ensure that the models produced by the system meet minimum criteria. Additional fitness criteria can be garnered by comparisons of the same data against previous models.
  • fitness can be defined as the accuracy of classification. This accuracy can be considered by the measure and type of correct and incorrect answers. This is a more complex operation when multiple classes are considered, as the system considers these rates in a relative manner, and may choose to perform degrees of closeness to intended results in addition to the standard accuracy tests
  • a sample can be classified in one of four categories (calculated per sample on a per class basis):
  • the goal is to maximize true positives on a per class basis.
  • the rate of error considers the tolerance for false positives and negatives.
  • Various adjustments to the model can be used to improve or further optimize the reduction of these levels of error.
  • an additional back test can be calculated across the entire population. This back-test can provide a more comprehensive analysis of the samples versus the entire population. If the complete back-test demonstrates a similar fitness characteristic to the model as calculated in the training set, and the fitness meets criteria, at this point, the model can be confirmed. Once a model is confirmed, the classification for each sample in the population can be updated via the unified data access layer 135 and the new model can be put into active participation via the classifier component 125 .
  • a general model requires calculation of several smaller specific case models. These models can defined as part of the overall model during its original genesis.
  • the sub models operate on a very similar overall process to the main model, only on smaller data sets and sets of features.
  • the general model can accommodate multiple sub models for particular specialized criteria.
  • Each sub model should be generated in a similar iterative manner. The final results of the sub models should be referenced in the main model, unless a sub model is judged to be entirely complete in its assessment, in which case it supplants the main model for classifications of this type.
  • Multiple models can be generated as follows. For each sub model required, an appropriate entire population can be selected. Thereafter, a random training set can be selected from the sub model population. In addition, a random testing set can be selected from the sub model population. Next, a model can be generated based on the appropriate training set. The fitness of the new sub model can be iteratively tested against defined criteria and previous models. Upon achieving an accurate model, the model can be published in conjunction with the general model.
  • FIG. 7 is a diagram 700 of a process flow diagram in which, at 705 , a sample of data is placed within a directed graph such as directed acyclic graph.
  • the directed graph comprises a plurality of hierarchical nodes that form a queue of work items for each particular worker class that is used to process the sample of data.
  • work items are scheduled within the queue for each of a plurality of workers by traversing the nodes of the directed graph.
  • the work items are then served, at 715 , to the workers according to the queue.
  • Results can later be received, at 720 , from the workers for the work items (the nodes of the directed graph are traversed based on the received results).
  • the results can be classified so that, at 730 , one or models can be generated.
  • this system can be used to make a determination of the likelihood that a particular computer file is malicious (intends harm to the operator or underlying computer system).
  • the system can be defined by samples representing files on a computer. These files can be normal program executables, data files, or any other type of file on a computer.
  • the classification system is tuned (using, for example, one or models that have been trained using historical file analyses with known outcomes) to model the “goodness” and “badness” of a potential sample, delivering a likelihood of a sample being something that could cause harm to a computer if executed.
  • the models are created from a set of features extracted from samples (using, for example, one or models that have been trained using historical file analyses with known outcomes). These features can include measurement about the file as well as its contents through several stages of analysis. Some example features include file size, information density, structured layout of the file, specific elements pertinent to the type of file it is (program section names for programs, author details for documents, etc.). The features can also include several layers of deeper analysis that can be represented as features within the system. This can include deep textual or code analysis, including interpreting instructions in an emulated fashion.
  • Sample classifications could be the likelihood of the presence of a cancerous growth within an image.
  • samples are represented by individual X-Ray images contained in high resolution image formatted computer files. These images are processed, collecting feature based data including orientation, size, shade differences, and linearity. These features are used to create models that offer strong ability to predict the existence or non existence of cancerous growths being detected in a particular image, and highlight those growths to a researcher or doctor.
  • One or more aspects or features of the subject matter described herein can be realized in digital electronic circuitry, integrated circuitry, specially designed application specific integrated circuits (ASICs), field programmable gate arrays (FPGAs) computer hardware, firmware, software, and/or combinations thereof.
  • ASICs application specific integrated circuits
  • FPGAs field programmable gate arrays
  • These various aspects or features can include implementation in one or more computer programs that are executable and/or interpretable on a programmable system including at least one programmable processor, which can be special or general purpose, coupled to receive data and instructions from, and to transmit data and instructions to, a storage system, at least one input device, and at least one output device.
  • the programmable system or computing system may include clients and servers.
  • a client and server are generally remote from each other and typically interact through a communication network. The relationship of client and server arises by virtue of computer programs running on the respective computers and having a client-server relationship to each other.
  • machine-readable signal refers to any signal used to provide machine instructions and/or data to a programmable processor.
  • the machine-readable medium can store such machine instructions non-transitorily, such as for example as would a non-transient solid-state memory or a magnetic hard drive or any equivalent storage medium.
  • the machine-readable medium can alternatively or additionally store such machine instructions in a transient manner, such as for example as would a processor cache or other random access memory associated with one or more physical processor cores.
  • one or more aspects or features of the subject matter described herein can be implemented on a computer having a display device, such as for example a cathode ray tube (CRT) or a liquid crystal display (LCD) or a light emitting diode (LED) monitor for displaying information to the user and a keyboard and a pointing device, such as for example a mouse or a trackball, by which the user may provide input to the computer.
  • a display device such as for example a cathode ray tube (CRT) or a liquid crystal display (LCD) or a light emitting diode (LED) monitor for displaying information to the user
  • LCD liquid crystal display
  • LED light emitting diode
  • a keyboard and a pointing device such as for example a mouse or a trackball
  • feedback provided to the user can be any form of sensory feedback, such as for example visual feedback, auditory feedback, or tactile feedback; and input from the user may be received in any form, including, but not limited to, acoustic, speech, or tactile input.
  • Other possible input devices include, but are not limited to, touch screens or other touch-sensitive devices such as single or multi-point resistive or capacitive trackpads, voice recognition hardware and software, optical scanners, optical pointers, digital image capture devices and associated interpretation software, and the like.
  • phrases such as “at least one of” or “one or more of” may occur followed by a conjunctive list of elements or features.
  • the term “and/or” may also occur in a list of two or more elements or features. Unless otherwise implicitly or explicitly contradicted by the context in which it used, such a phrase is intended to mean any of the listed elements or features individually or any of the recited elements or features in combination with any of the other recited elements or features.
  • the phrases “at least one of A and B;” “one or more of A and B;” and “A and/or B” are each intended to mean “A alone, B alone, or A and B together.”
  • a similar interpretation is also intended for lists including three or more items.
  • the phrases “at least one of A, B, and C;” “one or more of A, B, and C;” and “A, B, and/or C” are each intended to mean “A alone, B alone, C alone, A and B together, A and C together, B and C together, or A and B and C together.”
  • use of the term “based on,” above and in the claims is intended to mean, “based at least in part on,” such that an unrecited feature or element is also permissible.

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Cited By (33)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US9262296B1 (en) 2014-01-31 2016-02-16 Cylance Inc. Static feature extraction from structured files
US9269057B1 (en) * 2013-12-11 2016-02-23 Google, Inc. Using specialized workers to improve performance in machine learning
US9378012B2 (en) 2014-01-31 2016-06-28 Cylance Inc. Generation of API call graphs from static disassembly
US9465940B1 (en) 2015-03-30 2016-10-11 Cylance Inc. Wavelet decomposition of software entropy to identify malware
US9495633B2 (en) 2015-04-16 2016-11-15 Cylance, Inc. Recurrent neural networks for malware analysis
US9602531B1 (en) 2016-02-16 2017-03-21 Cylance, Inc. Endpoint-based man in the middle attack detection
US20170177627A1 (en) * 2015-12-22 2017-06-22 Intel IP Corporation Multi-label content recategorization
WO2017112235A1 (fr) * 2015-12-24 2017-06-29 Mcafee, Inc. Classification de contenu
US10063406B2 (en) * 2015-07-15 2018-08-28 TUPL, Inc. Automatic customer complaint resolution
US10235518B2 (en) 2014-02-07 2019-03-19 Cylance Inc. Application execution control utilizing ensemble machine learning for discernment
WO2019194787A1 (fr) * 2018-04-02 2019-10-10 Visa International Service Association Détection d'anomalie d'entité en temps réel
US20190383840A1 (en) * 2018-06-15 2019-12-19 Elemental Scientific, Inc. System for prioritization of collecting and analyzing liquid samples
US20200044938A1 (en) * 2018-07-31 2020-02-06 EMC IP Holding Company LLC Allocation of Shared Computing Resources Using a Classifier Chain
CN110956272A (zh) * 2019-11-01 2020-04-03 第四范式（北京）技术有限公司 实现数据处理的方法和系统
US10650492B2 (en) * 2017-09-08 2020-05-12 Baidu Online Network Technology (Beijing) Co., Ltd. Method and apparatus for generating image
US20200159690A1 (en) * 2018-11-16 2020-05-21 Sap Se Applying scoring systems using an auto-machine learning classification approach
US20200241917A1 (en) * 2017-10-13 2020-07-30 Huawei Technologies Co., Ltd. Resource management method and terminal device
US10733023B1 (en) * 2015-08-06 2020-08-04 D2Iq, Inc. Oversubscription scheduling
US10853079B2 (en) * 2018-09-26 2020-12-01 Side Effects Software Inc. Dependency-based streamlined processing
US10915529B2 (en) * 2018-03-14 2021-02-09 International Business Machines Corporation Selecting an optimal combination of systems for query processing
CN113312630A (zh) * 2021-05-31 2021-08-27 支付宝(杭州)信息技术有限公司 实现可信调度的方法及装置
US11164093B1 (en) * 2018-08-03 2021-11-02 Amazon Technologies, Inc. Artificial intelligence system incorporating automatic model switching based on model parameter confidence sets
US11170104B1 (en) * 2015-08-21 2021-11-09 Amazon Technologies, Inc. Identifying attacks on file systems
US11271796B2 (en) * 2016-07-15 2022-03-08 Tupl Inc. Automatic customer complaint resolution
CN114297460A (zh) * 2021-11-15 2022-04-08 北京众标智能科技有限公司 一种分布式动态可配置的爬虫平台及爬虫方法
TWI764905B (zh) * 2016-09-23 2022-05-21 南韓商三星電子股份有限公司 偵測物體的裝置與方法、製造處理器的方法以及建構積體電路的方法
US20220237293A1 (en) * 2015-05-12 2022-07-28 Webroot Inc. Automatic threat detection of executable files based on static data analysis
US11436056B2 (en) * 2018-07-19 2022-09-06 EMC IP Holding Company LLC Allocation of shared computing resources using source code feature extraction and clustering-based training of machine learning models
US20230015697A1 (en) * 2021-07-13 2023-01-19 Citrix Systems, Inc. Application programming interface (api) authorization
US11567807B2 (en) 2018-03-30 2023-01-31 EMC IP Holding Company LLC Allocation of shared computing resources using source code feature extraction and machine learning
US20230216868A1 (en) * 2019-09-27 2023-07-06 Musarubra Us Llc Analysis of endpoint detect and response data
WO2023161716A1 (fr) * 2022-02-24 2023-08-31 Nuix Limited Mise à l'échelle d'une grappe rest à l'aide d'une logique de traitement de tâche
US11790299B2 (en) * 2018-10-01 2023-10-17 Elemental Machines, Inc. Method and apparatus for process optimization

Families Citing this family (13)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
EP3014443B1 (fr) 2013-06-24 2020-06-10 Cylance Inc. Système automatisé pour une classification générative à multiples classes et multiples modèles, et analyse de similarité à l'aide d'un apprentissage automatique
US10193762B2 (en) * 2016-08-11 2019-01-29 Rescale, Inc. Dynamic optimization of simulation resources
US10387198B2 (en) 2016-08-11 2019-08-20 Rescale, Inc. Integrated multi-provider compute platform
US10565513B2 (en) * 2016-09-19 2020-02-18 Applied Materials, Inc. Time-series fault detection, fault classification, and transition analysis using a K-nearest-neighbor and logistic regression approach
CN110705975B (zh) * 2018-07-10 2021-06-18 马上消费金融股份有限公司 一种扣款指令发起方法及装置
KR102156439B1 (ko) * 2018-11-06 2020-09-16 한국전자기술연구원 클라우드-엣지 시스템 및 이의 데이터 처리 방법
CN111352965B (zh) * 2020-02-18 2023-09-08 腾讯科技（深圳）有限公司 序列挖掘模型的训练方法、序列数据的处理方法及设备
JP2021131310A (ja) * 2020-02-20 2021-09-09 東日本旅客鉄道株式会社 配管劣化診断装置及び配管劣化診断システム
US11645733B2 (en) 2020-06-16 2023-05-09 Bank Of America Corporation System and method for providing artificial intelligence architectures to people with disabilities
JP7170955B1 (ja) * 2020-12-07 2022-11-14 三菱電機株式会社 情報処理装置、情報処理方法及び情報処理プログラム
CN112817731B (zh) * 2021-02-25 2022-12-06 合肥工业大学 一种基于节点复制的异构多核系统任务调度方法
US12106148B2 (en) * 2022-09-16 2024-10-01 The Toronto-Dominion Bank Method and system for scheduling access to a node class of a shared resource
CN116910567B (zh) * 2023-09-12 2024-03-15 腾讯科技（深圳）有限公司 推荐业务的在线训练样本构建方法及相关装置

Citations (12)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US6430590B1 (en) * 1999-01-29 2002-08-06 International Business Machines Corporation Method and apparatus for processing executable program modules having multiple dependencies
US6748593B1 (en) * 2000-02-17 2004-06-08 International Business Machines Corporation Apparatus and method for starvation load balancing using a global run queue in a multiple run queue system
US20050049497A1 (en) * 2003-06-25 2005-03-03 Sriram Krishnan Systems and methods for automated diagnosis and decision support for breast imaging
US20060112388A1 (en) * 2004-11-22 2006-05-25 Masaaki Taniguchi Method for dynamic scheduling in a distributed environment
US7240048B2 (en) * 2002-08-05 2007-07-03 Ben Pontius System and method of parallel pattern matching
US20080133571A1 (en) * 2006-12-05 2008-06-05 International Business Machines Corporation Modifying Behavior in Messaging Systems According to Organizational Hierarchy
US20100082513A1 (en) * 2008-09-26 2010-04-01 Lei Liu System and Method for Distributed Denial of Service Identification and Prevention
US20100107170A1 (en) * 2008-10-29 2010-04-29 Curtis Stehley Group work sorting in a parallel computing system
US7937705B1 (en) * 2001-07-20 2011-05-03 I2Z Technology, Llc Dynamic job processing based on estimated completion time and specified tolerance time
US20120079490A1 (en) * 2010-09-23 2012-03-29 Microsoft Corporation Distributed workflow in loosely coupled computing
US20120221497A1 (en) * 2011-02-25 2012-08-30 Rajan Goyal Regular Expression Processing Automaton
US8887163B2 (en) * 2010-06-25 2014-11-11 Ebay Inc. Task scheduling based on dependencies and resources

Family Cites Families (60)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US5841947A (en) 1996-07-12 1998-11-24 Nordin; Peter Computer implemented machine learning method and system
US6546551B1 (en) 1999-09-28 2003-04-08 International Business Machines Corporation Method for accurately extracting library-based object-oriented applications
US7181768B1 (en) 1999-10-28 2007-02-20 Cigital Computer intrusion detection system and method based on application monitoring
US6556983B1 (en) 2000-01-12 2003-04-29 Microsoft Corporation Methods and apparatus for finding semantic information, such as usage logs, similar to a query using a pattern lattice data space
US6898737B2 (en) 2001-05-24 2005-05-24 Microsoft Corporation Automatic classification of event data
JP2005044330A (ja) 2003-07-24 2005-02-17 Univ Of California San Diego 弱仮説生成装置及び方法、学習装置及び方法、検出装置及び方法、表情学習装置及び方法、表情認識装置及び方法、並びにロボット装置
US7644441B2 (en) 2003-09-26 2010-01-05 Cigital, Inc. Methods for identifying malicious software
US7624449B1 (en) 2004-01-22 2009-11-24 Symantec Corporation Countering polymorphic malicious computer code through code optimization
US20060047807A1 (en) 2004-08-25 2006-03-02 Fujitsu Limited Method and system for detecting a network anomaly in a network
US7716645B2 (en) 2005-06-10 2010-05-11 International Business Machines Corporation Using atomic sets of memory locations
US7945902B1 (en) 2005-07-13 2011-05-17 Oracle America, Inc. Detection of non-standard application programming interface usage via analysis of executable code
US20080134326A2 (en) 2005-09-13 2008-06-05 Cloudmark, Inc. Signature for Executable Code
IL181041A0 (en) 2007-01-29 2007-07-04 Deutsche Telekom Ag Improved method and system for detecting malicious behavioral patterns in a computer, using machine learning
WO2007117636A2 (fr) 2006-04-06 2007-10-18 Smobile Systems, Inc. Système et procédé de détection de maliciels pour des données comprimées sur des plates-formes mobiles
WO2007135723A1 (fr) 2006-05-22 2007-11-29 Fujitsu Limited Dispositif d'apprentissage de réseau neuronal, procédé et programme
WO2008017991A2 (fr) 2006-08-11 2008-02-14 Koninklijke Philips Electronics, N.V. Procédés et appareils pour intégrer un format de conversion des données systématique en une sélection de sous-ensembles de caractéristiques à partir d'un algorithme génétique
WO2008055156A2 (fr) 2006-10-30 2008-05-08 The Trustees Of Columbia University In The City Of New York Procedes, supports et systemes permettant de detecter une sequence anormale d'appels de fonction
US8370818B2 (en) 2006-12-02 2013-02-05 Time Warner Cable Inc. Methods and apparatus for analyzing software interface usage
GB2444514A (en) 2006-12-04 2008-06-11 Glasswall Electronic file re-generation
US8069484B2 (en) 2007-01-25 2011-11-29 Mandiant Corporation System and method for determining data entropy to identify malware
CN101039177A (zh) 2007-04-27 2007-09-19 珠海金山软件股份有限公司 一种在线查毒的装置和方法
US7889651B2 (en) * 2007-06-06 2011-02-15 International Business Machines Corporation Distributed joint admission control and dynamic resource allocation in stream processing networks
KR100942795B1 (ko) 2007-11-21 2010-02-18 한국전자통신연구원 악성프로그램 탐지장치 및 그 방법
US8347272B2 (en) 2008-07-23 2013-01-01 International Business Machines Corporation Call graph dependency extraction by static source code analysis
US20100082400A1 (en) 2008-09-29 2010-04-01 Yahoo! Inc.. Scoring clicks for click fraud prevention
US20100107245A1 (en) 2008-10-29 2010-04-29 Microsoft Corporation Tamper-tolerant programs
JP2010165230A (ja) 2009-01-16 2010-07-29 Pharma Design Inc 薬剤標的となるタンパク質−タンパク質相互作用を予測する方法及び予測システム
US9239740B2 (en) 2009-06-16 2016-01-19 Microsoft Technology Licensing, Llc Program partitioning across client and cloud
US8726254B2 (en) 2009-06-20 2014-05-13 Microsoft Corporation Embedded annotation and program analysis
US8484152B2 (en) 2009-06-26 2013-07-09 Hbgary, Inc. Fuzzy hash algorithm
US8370613B1 (en) 2009-06-30 2013-02-05 Symantec Corporation Method and apparatus for automatically optimizing a startup sequence to improve system boot time
US8560465B2 (en) 2009-07-02 2013-10-15 Samsung Electronics Co., Ltd Execution allocation cost assessment for computing systems and environments including elastic computing systems and environments
US9081958B2 (en) 2009-08-13 2015-07-14 Symantec Corporation Using confidence about user intent in a reputation system
US8516452B2 (en) 2009-12-08 2013-08-20 International Business Machines Corporation Feedback-directed call graph expansion
US8818923B1 (en) 2011-06-27 2014-08-26 Hrl Laboratories, Llc Neural network device with engineered delays for pattern storage and matching
WO2012071989A1 (fr) 2010-11-29 2012-06-07 北京奇虎科技有限公司 Procédé et système pour une identification de programme fondée sur l'apprentissage machine
WO2012075336A1 (fr) 2010-12-01 2012-06-07 Sourcefire, Inc. Détection d'un logiciel malveillant par le biais d'informations contextuelles, de signatures génériques et de techniques d'apprentissage machine
US8549647B1 (en) 2011-01-14 2013-10-01 The United States Of America As Represented By The Secretary Of The Air Force Classifying portable executable files as malware or whiteware
US8402543B1 (en) * 2011-03-25 2013-03-19 Narus, Inc. Machine learning based botnet detection with dynamic adaptation
US9286182B2 (en) 2011-06-17 2016-03-15 Microsoft Technology Licensing, Llc Virtual machine snapshotting and analysis
US8631395B2 (en) 2011-09-02 2014-01-14 Microsoft Corporation Inter-procedural dead catch handler optimizations
US9329887B2 (en) 2011-10-19 2016-05-03 Hob Gmbh & Co. Kg System and method for controlling multiple computer peripheral devices using a generic driver
US20130152200A1 (en) 2011-12-09 2013-06-13 Christoph Alme Predictive Heap Overflow Protection
CN103186406B (zh) 2011-12-30 2016-08-17 国际商业机器公司 用于控制流分析的方法和装置
US8713684B2 (en) 2012-02-24 2014-04-29 Appthority, Inc. Quantifying the risks of applications for mobile devices
US8627291B2 (en) 2012-04-02 2014-01-07 International Business Machines Corporation Identification of localizable function calls
US8819772B2 (en) * 2012-06-25 2014-08-26 Appthority, Inc. In-line filtering of insecure or unwanted mobile device software components or communications
US9003529B2 (en) 2012-08-29 2015-04-07 The Johns Hopkins University Apparatus and method for identifying related code variants in binaries
US9292688B2 (en) 2012-09-26 2016-03-22 Northrop Grumman Systems Corporation System and method for automated machine-learning, zero-day malware detection
US20140180738A1 (en) 2012-12-21 2014-06-26 Cloudvu, Inc. Machine learning for systems management
US9104525B2 (en) 2013-01-22 2015-08-11 Microsoft Technology Licensing, Llc API usage pattern mining
US9015685B2 (en) 2013-03-01 2015-04-21 International Business Machines Corporation Code analysis for simulation efficiency improvement
US20140358828A1 (en) 2013-05-29 2014-12-04 Purepredictive, Inc. Machine learning generated action plan
US20140372513A1 (en) 2013-06-12 2014-12-18 Cloudvu, Inc. Multi-tenant enabling a single-tenant computer program product
EP3014443B1 (fr) 2013-06-24 2020-06-10 Cylance Inc. Système automatisé pour une classification générative à multiples classes et multiples modèles, et analyse de similarité à l'aide d'un apprentissage automatique
EP2833594A1 (fr) 2013-07-31 2015-02-04 Siemens Aktiengesellschaft Procédé et système de détection d'intrusion de réseaux neuronaux à trois niveaux sur la base de caractéristiques
US9753959B2 (en) 2013-10-16 2017-09-05 University Of Tennessee Research Foundation Method and apparatus for constructing a neuroscience-inspired artificial neural network with visualization of neural pathways
US9262296B1 (en) 2014-01-31 2016-02-16 Cylance Inc. Static feature extraction from structured files
US8930916B1 (en) 2014-01-31 2015-01-06 Cylance Inc. Generation of API call graphs from static disassembly
US20150248556A1 (en) 2014-02-28 2015-09-03 Government Of The United States, As Represented By The Secretary Of The Air Force Firmware Disassembly System

Patent Citations (12)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US6430590B1 (en) * 1999-01-29 2002-08-06 International Business Machines Corporation Method and apparatus for processing executable program modules having multiple dependencies
US6748593B1 (en) * 2000-02-17 2004-06-08 International Business Machines Corporation Apparatus and method for starvation load balancing using a global run queue in a multiple run queue system
US7937705B1 (en) * 2001-07-20 2011-05-03 I2Z Technology, Llc Dynamic job processing based on estimated completion time and specified tolerance time
US7240048B2 (en) * 2002-08-05 2007-07-03 Ben Pontius System and method of parallel pattern matching
US20050049497A1 (en) * 2003-06-25 2005-03-03 Sriram Krishnan Systems and methods for automated diagnosis and decision support for breast imaging
US20060112388A1 (en) * 2004-11-22 2006-05-25 Masaaki Taniguchi Method for dynamic scheduling in a distributed environment
US20080133571A1 (en) * 2006-12-05 2008-06-05 International Business Machines Corporation Modifying Behavior in Messaging Systems According to Organizational Hierarchy
US20100082513A1 (en) * 2008-09-26 2010-04-01 Lei Liu System and Method for Distributed Denial of Service Identification and Prevention
US20100107170A1 (en) * 2008-10-29 2010-04-29 Curtis Stehley Group work sorting in a parallel computing system
US8887163B2 (en) * 2010-06-25 2014-11-11 Ebay Inc. Task scheduling based on dependencies and resources
US20120079490A1 (en) * 2010-09-23 2012-03-29 Microsoft Corporation Distributed workflow in loosely coupled computing
US20120221497A1 (en) * 2011-02-25 2012-08-30 Rajan Goyal Regular Expression Processing Automaton

Non-Patent Citations (5)

* Cited by examiner, † Cited by third party
Title
Wikipedia page for Data Type, retrieved 7/2015, URL: https://en.wikipedia.org/wiki/Data_type *
Wikipedia page for File System, retrieved 7/2015, URL: https://en.wikipedia.org/wiki/File_system *
Wikipedia, FIFO (computing and electronics) webpage, downloaded 07/2016 *
Wikipedia, Queue (abstract data type) webpage, downloaded 07/2016 *
Wikipedia, Scheduling (computing) webpage, downloaded 07/2016 *

Cited By (48)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US9269057B1 (en) * 2013-12-11 2016-02-23 Google, Inc. Using specialized workers to improve performance in machine learning
US9262296B1 (en) 2014-01-31 2016-02-16 Cylance Inc. Static feature extraction from structured files
US9378012B2 (en) 2014-01-31 2016-06-28 Cylance Inc. Generation of API call graphs from static disassembly
US9921830B2 (en) 2014-01-31 2018-03-20 Cylance Inc. Generation of API call graphs from static disassembly
US9959276B2 (en) 2014-01-31 2018-05-01 Cylance Inc. Static feature extraction from structured files
US10235518B2 (en) 2014-02-07 2019-03-19 Cylance Inc. Application execution control utilizing ensemble machine learning for discernment
US9465940B1 (en) 2015-03-30 2016-10-11 Cylance Inc. Wavelet decomposition of software entropy to identify malware
US9946876B2 (en) 2015-03-30 2018-04-17 Cylance Inc. Wavelet decomposition of software entropy to identify malware
US9495633B2 (en) 2015-04-16 2016-11-15 Cylance, Inc. Recurrent neural networks for malware analysis
US10691799B2 (en) 2015-04-16 2020-06-23 Cylance Inc. Recurrent neural networks for malware analysis
US10558804B2 (en) 2015-04-16 2020-02-11 Cylance Inc. Recurrent neural networks for malware analysis
US20220237293A1 (en) * 2015-05-12 2022-07-28 Webroot Inc. Automatic threat detection of executable files based on static data analysis
US20180367370A1 (en) * 2015-07-15 2018-12-20 Tupl Inc. Automatic customer complaint resolution
US10680875B2 (en) * 2015-07-15 2020-06-09 Tupl Inc. Automatic customer complaint resolution
US10397043B2 (en) 2015-07-15 2019-08-27 TUPL, Inc. Wireless carrier network performance analysis and troubleshooting
US10063406B2 (en) * 2015-07-15 2018-08-28 TUPL, Inc. Automatic customer complaint resolution
US10733023B1 (en) * 2015-08-06 2020-08-04 D2Iq, Inc. Oversubscription scheduling
US11220688B2 (en) 2015-08-06 2022-01-11 D2Iq, Inc. Oversubscription scheduling
US11170104B1 (en) * 2015-08-21 2021-11-09 Amazon Technologies, Inc. Identifying attacks on file systems
US20170177627A1 (en) * 2015-12-22 2017-06-22 Intel IP Corporation Multi-label content recategorization
US10691739B2 (en) * 2015-12-22 2020-06-23 Mcafee, Llc Multi-label content recategorization
WO2017112235A1 (fr) * 2015-12-24 2017-06-29 Mcafee, Inc. Classification de contenu
US9602531B1 (en) 2016-02-16 2017-03-21 Cylance, Inc. Endpoint-based man in the middle attack detection
US11271796B2 (en) * 2016-07-15 2022-03-08 Tupl Inc. Automatic customer complaint resolution
TWI764905B (zh) * 2016-09-23 2022-05-21 南韓商三星電子股份有限公司 偵測物體的裝置與方法、製造處理器的方法以及建構積體電路的方法
US10650492B2 (en) * 2017-09-08 2020-05-12 Baidu Online Network Technology (Beijing) Co., Ltd. Method and apparatus for generating image
US20200241917A1 (en) * 2017-10-13 2020-07-30 Huawei Technologies Co., Ltd. Resource management method and terminal device
US11693693B2 (en) * 2017-10-13 2023-07-04 Huawei Technologies Co., Ltd. Resource management based on ranking of importance of applications
US10915529B2 (en) * 2018-03-14 2021-02-09 International Business Machines Corporation Selecting an optimal combination of systems for query processing
US11567807B2 (en) 2018-03-30 2023-01-31 EMC IP Holding Company LLC Allocation of shared computing resources using source code feature extraction and machine learning
US12041140B2 (en) 2018-04-02 2024-07-16 Visa International Service Association Real-time entity anomaly detection
WO2019194787A1 (fr) * 2018-04-02 2019-10-10 Visa International Service Association Détection d'anomalie d'entité en temps réel
US20190383840A1 (en) * 2018-06-15 2019-12-19 Elemental Scientific, Inc. System for prioritization of collecting and analyzing liquid samples
US11531038B2 (en) * 2018-06-15 2022-12-20 Elemental Scientific Inc. System for prioritization of collecting and analyzing liquid samples
US11436056B2 (en) * 2018-07-19 2022-09-06 EMC IP Holding Company LLC Allocation of shared computing resources using source code feature extraction and clustering-based training of machine learning models
US20200044938A1 (en) * 2018-07-31 2020-02-06 EMC IP Holding Company LLC Allocation of Shared Computing Resources Using a Classifier Chain
US10862765B2 (en) * 2018-07-31 2020-12-08 EMC IP Holding Company LLC Allocation of shared computing resources using a classifier chain
US11164093B1 (en) * 2018-08-03 2021-11-02 Amazon Technologies, Inc. Artificial intelligence system incorporating automatic model switching based on model parameter confidence sets
US10853079B2 (en) * 2018-09-26 2020-12-01 Side Effects Software Inc. Dependency-based streamlined processing
US11790299B2 (en) * 2018-10-01 2023-10-17 Elemental Machines, Inc. Method and apparatus for process optimization
US20200159690A1 (en) * 2018-11-16 2020-05-21 Sap Se Applying scoring systems using an auto-machine learning classification approach
US20230216868A1 (en) * 2019-09-27 2023-07-06 Musarubra Us Llc Analysis of endpoint detect and response data
CN110956272A (zh) * 2019-11-01 2020-04-03 第四范式（北京）技术有限公司 实现数据处理的方法和系统
CN113312630A (zh) * 2021-05-31 2021-08-27 支付宝(杭州)信息技术有限公司 实现可信调度的方法及装置
US20230015697A1 (en) * 2021-07-13 2023-01-19 Citrix Systems, Inc. Application programming interface (api) authorization
CN114297460A (zh) * 2021-11-15 2022-04-08 北京众标智能科技有限公司 一种分布式动态可配置的爬虫平台及爬虫方法
WO2023161716A1 (fr) * 2022-02-24 2023-08-31 Nuix Limited Mise à l'échelle d'une grappe rest à l'aide d'une logique de traitement de tâche
WO2023164106A1 (fr) * 2022-02-24 2023-08-31 Nuix Limited Mise à l'échelle d'une grappe de repos à l'aide d'une direction basée sur des caractéristiques

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